code: def __init__(self, ctx: Context, local_mode: bool = False) -> None:
      super().__init__(ctx, local_mode)
  
imports: from fate.ml.nn.trainer.trainer_base import HomoTrainerClient, HomoTrainerServer
  from fate.ml.nn.trainer.trainer_base import FedArguments, TrainingArguments
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Callable, Union
  from fate.arch import Context
  from torch.optim import Optimizer
  from torch.utils.data import Dataset
  from torch.optim.lr_scheduler import _LRScheduler
  from transformers.trainer_callback import TrainerCallback
  from torch.nn import Module
  from torch import nn
  from torch.utils.data import DataLoader
  from transformers import TrainerState, TrainerControl, PreTrainedTokenizer
  from fate.ml.aggregator import AggregatorClientWrapper, AggregatorServerWrapper
  import logging
label_desc: Use image node_id 1 to create a new FedAVGServer object from inherited base classes: HomoTrainerServer with example: obj = FedAVGServer(ctx, local_mode)